Simulating policy alternatives for public pension in Japan (pdf)

Simulating policy alternatives for public pension in Japan (pdf)

Simulating policy alternatives for public pension in Japan (pdf)


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2. Outline of INAHSIM2.1. Development of dynamic microsimulation models for JapanINAHSIM is a dynamic microsimulation model developed specifically for Japan. This model wasinitially developed in the first half of the 1980s as a tool for household simulation. Following severalattempts 3 to improve the model and to add socioeconomic characteristics of the population, the latestversion of INAHSIM has been utilized as a microsimulation model for policy simulation.The first version (Aoi et al., 1986 and Inagaki, 1986) was limited to the household simulation. Itsimulated only kinship and co-resident relationships. It incorporated the demographic events of birth,death, marriage, and divorce, and a few household movements of "young people leaving home" and"living with elderly parents." The size of the initial population was 32,000 persons and 10,000households. It took one hour for 50-year simulation by a mainframe computer since the performanceof computers at that time was poor for the simulation.The second version (Inagaki, 2005) was extended its capability for policy simulation. Thesocioeconomic characteristics of employment status, health status, and earnings are added, and thesize of the initial population was increased to 126,000 persons and 46,000 household. Imputation ofkinship relationships between the persons living in different households was newly introduced. Itimproved the quality of the results as household simulation. The impact of the increase in nonregularemployment on income disparities (Inagaki, 2007b) was evaluated by using the secondversion model.The third version (Inagaki and Kaneko, 2008 and Inagaki, 2010a) was a major revision ofINAHSIM. The public pension scheme was incorporated, and it was applied to the evaluation of theeffect of some proposals for basic pension reform on the income distribution of the elderly (Inagaki,2010b). It was introduced a new technique to align the initial population with the Population Census,and obtained better consistency with the official population projections (Kaneko et al., 2008), andhousehold projections (IPSS, 2008) prepared by the National Institute of Population and SocialSecurity Research. It also added two life events of international migration and payment of pensionpremium, and most transition probabilities are revised based on people's recent behavior. Inparticular, the transition probabilities of employment status are assumed to be consistent with theassumptions on the 2009 actuarial valuation on Employees' Pension Insurance and National Pension(Actuarial Affaires Division, Pension Bureau, Ministry of Health, Labor and Welfare, 2009).Consequently, the simulation results are very close to the official results.3 Fukawa (1994, 2007, 2009), Inagaki (2005, 2007a, 2010a), and Inagaki and Kaneko (2008) made attempts toimprove INAHSIM.5

100 years. It is also applicable to this model and consequently, the model population is designed onthe basis of such registers.Therefore, the model population comprises three tables 5 that correspond to the Family Register,Basic Resident Register, and individual socioeconomic characteristics. In INAHSIM, these threetables are referred to as “couple segment,” “household segment,” and “individual segment,”respectively. As depicted in Figure 2, there are links between the family and individual segments andbetween the household and individual segments.Figure 2. Basic structure of the model populationParents-childrenHusband-wifeIndividual segmentHousehold MembershipCouple segmentHousehold segmentIn this model, a family comprises a couple and their children. The couple segment has personID numbers for the husband, wife, and the youngest child (if any). It also includes certaincharacteristics of the couple such as the year of marriage, number of children, (if dissolved) the yearof dissolution, and cause of dissolution (divorce or death of a spouse). A group of children is definedby a list structure. Figure 3 depicts a family comprising a couple–Jim and Mary–and their threechildren–Ken, Karen, and Tom.Figure 3. A family comprising a couple and three children(Couple segment)Youngestchild’s IDHusband’sID (Jim)Wife’s ID(Mary)Other characteristics of the familyChild_1(Ken)Child_2(Karen)Child_3(Tom)(Individual segments)5 See Appendix A.7

Transition probabilities for each life event are given in advance, and it is possible to take intoaccount their future trends. In the assumptions of the baseline scenario described later, decliningtrends in first marriage rates and mortality rates are assumed. The future trends in the transitionprobabilities of employment status are also taken in account. The other transition probabilities of thebaseline scenario are assumed to continue in the future. Appendix B summarizes the transitionprobabilities.The key life events used in the simulation for the income distribution of the elderly are "Livingwith elderly parents," "Estimating earnings," and "Determining pensions."The first event is living with elderly parents. When elderly people, who do not live with theirchildren, become very old and need care, many children move in to take care of them. This is animportant life event to secure the life of the elderly in Japan.The second event is estimating earnings. Earnings are assumed to conform to a log-normaldistribution by sex, age group, and employment status. The earnings of each person are calculated byformula (2) using one’s z-score. The z-score represents one’s ability to earn money, and it is constantthroughout one’s life. The z-score is assumed to be determined at birth on the basis of one’s parents’z-scores. An immigrant’s z-score is assigned randomly at his/her entry. The z-scores of the initialpopulation are estimated on the basis of their earnings in the year 2004 by sex, age group, andemployment status.Earnings= exp( Mean + SD×[z −score]) .......... .......... .......... .......... .......... .......... .................(2)The third event is determining pensions. The pensionable age for basic pension is 65 years. Thepensionable age for earnings-related benefits is statutorily fixed at 60–65 years and is specified bysex and year of birth. Early and deferred pensions are not considered. The pension amount isestimated on the basis of the pensioner’s percent rank, sex, subscription category, and employmentstatus at the age of 35, assuming the distribution of the newly awarded pension amounts. The percentrank is equivalent to his/her z-score. The pension amount distributions under the current pensionsystem are shown in Table 1 for basic pension and Table 2 for earnings-related pension.10

Table 1. Basic pension amount by sex, category, and employment statusPercentrank Non-regularemployeesMaleUnemploye Category 2 Category 3 Non-regulardemployeesFemaleUnemploye Category 2 Category 3d5 % 0 0 0 622,400 579,200 0 0 0 518,200 579,20010 % 0 369,368 0 676,600 607,300 0 336,476 0 607,300 607,30020 % 411,149 579,500 0 699,700 656,800 347,286 642,800 0 674,100 656,80030 % 579,500 641,600 330,574 711,200 679,900 646,300 700,800 298,643 716,200 679,90040 % 653,500 699,700 420,101 722,800 703,000 710,900 729,400 352,690 732,700 703,00050 % 704,400 706,300 617,200 731,000 721,100 736,000 742,600 679,900 745,900 721,10060 % 722,800 739,300 699,700 740,900 734,300 746,300 759,096 729,400 759,100 734,30070 % 747,500 747,500 729,392 749,200 749,200 773,900 773,900 749,200 772,300 749,20080 % 785,500 790,500 747,500 777,200 769,000 785,500 786,600 773,900 782,200 769,00090 % 800,000 800,000 800,000 800,000 787,100 800,000 800,000 790,500 787,100 787,10095 % 800,000 800,000 800,000 800,000 790,500 800,000 800,000 800,000 800,000 790,500(Source) Estimated by the author using the Internet Survey on the Individual Records of Regular Pension CoverageNotice (Inagaki, 2010c)Table 2. Earnings-related pension amount by sex, category, and employment statusPercentrank Non-regularemployeesMaleUnemploye Category 2 Category 3 Non-regulardemployeesCategory 1SelfemployedCategory 1SelfemployedCategory 1SelfemployedCategory 1SelfemployedFemaleUnemploye Category 2 Category 3d5 % 0 0 0 465,999 0 0 0 0 111,711 010 % 0 0 0 573,891 0 0 0 0 210,746 020 % 0 0 0 730,177 0 0 0 0 288,877 030 % 0 0 0 901,799 0 0 0 0 370,455 040 % 0 0 0 1,031,812 0 0 0 0 430,669 050 % 0 0 0 1,159,975 0 0 0 0 486,099 060 % 0 0 0 1,262,539 29,863 42,481 42,481 42,481 590,732 29,86370 % 96,231 96,231 96,231 1,379,047 62,687 77,080 77,080 77,080 708,744 62,68780 % 217,007 217,007 217,007 1,491,353 92,787 133,415 133,415 133,415 775,345 92,78790 % 554,968 554,968 554,968 1,586,536 155,527 275,749 275,749 275,749 1,009,549 155,52795 % 666,407 666,407 666,407 1,650,859 218,729 412,552 412,552 412,552 1,123,861 218,729(Source) Estimated by the author using the Internet Survey on the Individual Records of Regular Pension CoverageNotice (Inagaki, 2010c)2.4. Compiling statisticsThis model produces a longitudinal micro dataset of individuals, families, and households for thefuture. Many basic statistics such as population statistics or vital statistics are compiled during the11

simulation process. Other special statistics or statistical analyses, if necessary, can be made using thelongitudinal micro data output independently from the simulation process.Stochastic errors derived from the Monte Carlo method can also be estimated by repeatingsimulations with different sets of random numbers.Table 3 shows the stochastic errors derived from the Monte Carlo method. This simulation takesan average of 100 simulation runs with the initial population of 128,000 persons. Therefore, thesubstantive size of the initial population is very large––12,800,000 persons, and stochastic errors arenegligible.However, the initial population itself has a sampling error, and the transition probabilitiesthemselves have errors when they are estimated. Moreover, people's behavior may considerablychange in the future. It is noted that these figures do not show the level of errors in these simulationresults, but merely show the stochastic errors derived from the Monte Carlo method.Table 3: Stochastic errorsEstimateYear 2025 Year 2050StandarderrorStasndarderror rateEstimateStandarderrorStasndarderror ratePopulation (in thousand)Total 120,057 18 0.01% 96,061 66 0.07%under 15 12,206 5 0.04% 8,524 9 0.11%15 - 64 71,278 2 0.00% 49,694 22 0.04%65 and over 36,574 5 0.01% 37,843 9 0.02%Houshold (in thousand)Number 51,057 4 0.01% 43,348 8 0.02%Average Size 2.30 0.00 0.00% 2.14 0.00 0.00%Household income (in ten thousand yen)Average 522.1 0.8 0.15% 481.5 1.9 0.40%Median 393.4 0.9 0.23% 328.6 1.7 0.50%Gini coefficient 0.455 0.000 0.00% 0.486 0.000 0.00%2.5. Computer language and execution timeThis model is written in FORTRAN90. If the initial population is 127,782 persons, that is, 1/1000 ofJapan's population, it takes about 30 seconds to make a 100-year simulation using a PC with 12GBRAM and an Intel® Core i7 975 Extreme Edition 3.33GHz processor. Since the execution time isrelatively short, it usually takes an average of 100 simulation runs to evaluate the simulation results.Appendix C summarizes the list of modules and CPU time.12

2.6. Initial population2.6.1. Source of the initial populationThe Comprehensive Survey of the Living Conditions (CSLC) conducted by the Ministry of Health,Labor, and Welfare is the main source of the initial population 7 . The survey is conducted every threeyears using large sample sizes. In the 2004 survey, the sample size was 25,091 households and72,487 household members. The survey covers kinship relationships within household members,marital status, employment status, health status, earnings, pension amounts, and other socioeconomiccharacteristics. The initial population of 49,307 private households and 126,570 household membersis prepared by resampling with replacement from the micro data. The elderly population of 1,212persons in institutional households is prepared separately and is added to the initial population. In theend, the initial population is 127,782 persons, and reflects Japan’s society on a 1/1000 scale.However, some information–for example, the kinship relationships between the persons livingin different households, histories of employment status and earnings, nationality, and so on–cannotbe obtained from CSLC. Such information is imputed.Another problem with CSLC is its collection rate. It was 54.7% in the 2004 survey; note thatthis rate varies according to sex, age, and household structure. The collection rate of single-personhouseholds was very low, and that of young people was also very low. These differences are adjustedby weighing the resampling rates when the initial population was prepared.2.6.2. Adjustment of collection rate and resampling with replacementSince the collection rates of CSLC differs with the characteristics of persons or households, it isnecessary to prepare the initial population by resampling the mother sample for consistency with thePopulation Census. However, the alignment of the initial population is not easy since we should alignboth the number of households and number of persons with census data. The iteration method is usedin this model.Repeat steps (a) to (c) until the adjustment rates are convergent. In our case, it took about 100times to be convergent.7 The data used in this study were made available to the author by the Ministry of Health, Labor, and Welfare ofJapan, notice number No.0907-7 dated 7 September 2010.13

(a) Estimate (1) the numbers of persons by sex, age group and marital status, and (2) the numbers ofhousehold by sex and age group of the head of the household, and household structure using theadjustment rate for each household calculated in step (c).(b) Compare the estimates with the Population Census, and recalculate the adjustment rates of thesampling fractions for (1) persons by sex, age group and marital status, and (2) households bysex and age group of the head of the household and household structure.(c) Take an average of the adjustment rates in step (b) for each household and these averages areapplied as the new adjustment rates in step (a).2.6.3. Imputation of kinship relationships between the persons living in different householdsAs discussed in section 2.2, the two couple segments (Figure 3 and Figure 4) are essential to specifythe kinship relationships. This means that all of the kinship relationships will be specified if theparent-child relationships are specified among the initial population. Here, the question is how toimpute the parent-child relationships among the persons living in different households. Theimputation method is as follows:(a) List the persons or couples who have children but live separately using the CSLC results. CSLCsurveyed the number of children who live separately for each person.(b) Randomly draw children whose parent(s) would be alive using the probabilities by child’s agethat his/her mother or father is alive. These probabilities can be estimated from the life tablesusing the average age difference between parents and children.(c) Make a match between the couples on the list (a) and the children on the list (b) in order of age.2.6.4. Imputation of other characteristicsWith regard to earnings, the micro data of CSLC is modified because it surveyed the earnings in theprevious year 8 ; consequently, the earnings are inconsistent with other characteristics such asemployment status. Specifically, the earnings are imputed in the same way as the life event of"estimating earnings."The personal histories of employment status are imputed by applying the transition probabilitiesretroactively. Those of earnings are imputed in the same way as the life event of "estimatingearnings."8 The earnings in the survey indicate the amount of earnings in the year 2003, and the employment status indicatesthe employment status as of June 1, 2004.14

The pension amounts of pensioners are also inconsistent with their age or employment statussince it surveyed the amount in the previous year. These are imputed in the same way as the lifeevent of "determining pensions."Nationality is assigned randomly using the percentage of non-Japanese population 9 by sex andage.3. Prospects of the elderly in the future3.1. Population aging rates by co-resident family typeFigure 7 shows the future trends in population aging rates of the elderly by co-resident family type,divided into the following subtypes: single-person households, couple-only households, those livingwith married children, those living with unmarried children, other private households, and those in aninstitution. The population aging rate will increase from 19.6% in the year 2004 to 31.8% in 2030,39.4% in 2050, and 41.0% in 2100.Figure 7. Trends in population aging rate by co-resident family type9 According to the 2005 Population Census, the percentage of non-Japanese population was 1.2%.15

In the future, the elderly living alone will increase significantly. It will reach 13.2% of thepopulation by the year 2100. The elderly living with unmarried children is also increasing by a largemargin, while those living with married children will decrease. These unmarried children are a futurecase of today’s “parasite singles 10 .” This is a case of parents becoming elderly while children are notable to become independent of their parental roof because they do not have sufficient economicresources due to their unstable employment and, therefore, continue to live with their parents withoutgetting married.3.2. The distribution of public pension amountsFigure 8 shows future trends in the distribution of public pension amounts to the elderly for the casein which the current pension scheme is maintained. We see a peak of 0.75–0.99 million yen after theyear 2015, consisting mainly of former category 1 and 3 subscribers. A flat distribution between 1.00and 2.24 million yen is observed, This is formed by former subscribers of Employees’ PensionInsurance (category 2) and beneficiaries of survivors’ pension.Figure 8. Trends in distribution of public pension amounts10 A Japanese-English term for single adults who live with their parents and do not marry until their late twenties orthirties.16

The figure also shows that the number of elderly people with very low pensions will notincrease even though the elderly will increase in number significantly. This is because of thematurity of the public pension scheme and the establishment of pension rights for dependent wives,which was introduced by an amendment in 1985. Since the participation of dependent wives in thescheme before 1985 was voluntarily, some of them would not be eligible for the public pension.After the amendment, their participation became compulsory as category 3 subscribers, and theirbasic pension eligibility is guaranteed.On the other hand, the elderly with very high pensions will significantly decrease in number.This decline is thought to be caused by a reduction in the pension level for men due to the pensionfairness adjustment and the transfer of a part of their pension to their wives’ name as basic pensionby the 1985 amendment.3.3. The distribution of equivalent incomeFigure 9 shows the future trends in the distribution of equivalent income. Equivalent income isdefined as the total income of the co-resident family divided by the square root of the number ofhousehold members. This represents the income level of the elderly adjusted by household size.Figure 9. Trends in distribution of equivalent income17

In 2004, the distribution is wide due to the variety of living arrangements for the elderly and thebroad distribution of their pension amounts. However, the distribution will have a clear peak around1.75–1.99 million yen after the year 2030, and shows no increase in the number of elderly peoplewith high equivalent income. On the other hand, the number of the elderly with low equivalentincome will increase considerably.Even though the public pension level for the low-pension group will improve, their equivalentincome will seemingly not improve. This may be because the reduction in private support from theirco-resident families as a result of the increase in the number of elderly people living alone. In themidst of the decline in the Japanese population, an increase in the numbers of the low-income elderlycauses concern because it will have a significant effect on Japanese society.3.4. Future trends in the poor elderly in the populationThe definition of poverty is not concrete, but here we consider an equivalent income of one millionyen as the poverty line. If their equivalent income is less than one million yen in Japan 11 , it is usuallyvery difficult for the elderly to maintain their life without any financial assistance.Figure 10 shows the future trends in the percentage of the poor elderly in the population by theirco-resident family type. The percentage will increase from 2.4% in the year 2004 to 3.6% in 2030,4.8% in 2050, and 5.5% in 2100. Most of the poor elderly represent single-person households. Thismeans that the current pension scheme will not provide sufficient income security for the elderly inthe future due to the significant change in their family arrangement.11 The equivalent income of the elderly couple with full amount of basic pension is about 1,120,000 yen. Onemillion yen is far below the threshold of public assistance.18

Figure 10. Trends in percentage of the poor elderly in the population4. Proposed pension reform plans and their evaluation4.1. Proposed pension reform plansThere are many proposals for public pension reform. Here, we consider four reform plans: (A) auniform earnings-related pension with a minimum guaranteed pension, proposed by the DPJ; (B) afully tax-funded basic pension system, addressed by the National Council for Social Security (2008);(C) a modified version of plan B, based on the proposal of Takayama (2010); and (D) a partially taxfundedbasic pension system, proposed by Inagaki (2010d).The idea of plan A is depicted in Figure 11. The minimum pension amount was 840,000 yen inthe DPJ manifesto. However, the specific values of the level of earnings-related pension or thethreshold amount to provide the minimum pension is not given. In this paper, we assume the specificvalues of variables in formulas (3) to (6). The following new formulas are applied to the insuredperiod only after fiscal year 2015. The pension amount corresponding to the insured period before2014 is calculated under the current scheme.ERP = [ average earnings]× 50%.......... .......... .......... .......... .......... .......... .......... ..................(3)MGP = 840 ,000if ERP

MGP = 840 ,000−(ERP−500,000)× 0.75 if 500,000≤ERP

[premium paid period before 2014] + [insured period after 2015]Pension = 792,100×(1 − p)×480 months+ 792,100×p ......................................................................................................... (8)The idea of plan D is to immediately introduce a fully tax-funded basic pension system for thelate-stage elderly (age 75 and over), and maintain the framework of the current system for the basicpension of the early-stage elderly (age 65 to 74). However, the basic pension for the early-stageelderly is fully financed by insurance premium. The pension formula of this plan is given byformulas (9) and (10).premiumpaid periodPension = 792 ,100 ×480 monthsPension = 792,100for theearly stage elderly ............................for the late stage elderly..........................(9)(10)Plan D may raise concerns of fairness between persons who paid past premiums and those whodid not pay. However, it is not a big problem because the actual past payments of insurancepremiums are reflected in the basic pension of the early-stage elderly. Actually, the basic pensionbenefits for 10 years, from age 65 to 74 amount to about 8 million yen, which exceeds the totalpremium contributions of 40 years at the current premium level of 15,100 yen per month.4.2. Future trends in percentage of the poor elderly in the populationThe most important purpose of public pension reform is the improvement of income security,especially for the poor elderly. It can be evaluated by simulating the percentage of the poor elderly inthe population. Table 4 compares its future trends among the current scheme and the proposedreform plans. If the current scheme is maintained, the percentage of the poor elderly will increasefrom 2% level of the population in 2004 to 5% level after 2050, posing a serious problem forJapanese society.All reform plans have the effect of reducing the proportion of poor elderly people significantly.However, the level and progress of the effect of each plan is very different. In the ultra long-termperiod, plan A has the most significant effect—for example, a 3.6 point reduction in 2100. On theother hand, in the mid- and long-term period, say, by 2030, plan D is the most effective, and plan Cthe next. Plans A and B have very little effect.The difference in progress of the effects is due to the plans’ transition process. Plans A and Bapply the new pension formula only to the insured period after fiscal year 2015, and the currentformula is applied to the period before 2014. It will take a very long time for the new pension21

formula to affect the pension amount of the elderly. The existing poor elderly may never reap thebenefit of this reform. Therefore, if we opt for plan A or B, it will become necessary to undertakeanother measure for the poor elderly.Table 4. Trends in percentage of the poor elderly in the populationNumber of the poor elderly (in thousand) Percentage of the poor elderly in the populationCurrent Plan A Plan B Plan C Plan D Current Plan A Plan B Plan C Plan D2004 3,127 3,127 3,127 3,127 3,127 2.4 % 2.4 % 2.4 % 2.4 % 2.4 %2010 3,368 3,368 3,368 3,368 3,368 2.6 % 2.6 % 2.6 % 2.6 % 2.6 %2020 3,898 3,896 3,897 3,489 2,904 3.2 % 3.2 % 3.2 % 2.8 % 2.4 %2030 4,154 3,989 4,105 3,593 3,281 3.6 % 3.4 % 3.5 % 3.1 % 2.8 %2040 4,435 3,582 4,204 3,538 3,677 4.2 % 3.4 % 3.9 % 3.3 % 3.4 %2050 4,652 2,892 4,171 3,499 3,762 4.8 % 3.0 % 4.3 % 3.6 % 3.9 %2060 4,613 2,230 3,886 3,431 3,706 5.4 % 2.6 % 4.5 % 4.0 % 4.3 %2070 4,120 1,650 3,244 3,055 3,321 5.5 % 2.2 % 4.4 % 4.1 % 4.5 %2080 3,570 1,294 2,676 2,630 2,856 5.6 % 2.0 % 4.2 % 4.1 % 4.5 %2090 3,077 1,085 2,274 2,270 2,453 5.5 % 2.0 % 4.1 % 4.1 % 4.4 %2100 2,626 923 1,939 1,939 2,103 5.5 % 1.9 % 4.0 % 4.0 % 4.4 %4.3. Total expenditure and additional cost of the public pension reform plansThe Japanese economy is hardly expected to grow in the future since the population will decreasewhile aging faster. It will be difficult to bear a large expenditure for the public pension in the future.Table 5 compares the future trends in total expenditure of the public pension among the currentscheme and the proposed reform plans. The total expenditure under the current scheme will reach themaximum level in 2020 and stay at that level by 2050. However, according to the actuarial valuationby the Ministry of Health, Labor and Welfare, Pension Bureau, Actuarial Division (2010), it isfinancially sustainable under an intermediate or optimistic economic assumption. Though there is adebate on its economic assumption, we assume that the current system is financially sustainable. Theadditional costs of the proposed reform plans will be essential for evaluating its sustainability.22

Table 5. Total expenditure and additional cost (in trillion yen)Total expenditure of public pensionAdditional costCurrent Plan A Plan B Plan C Plan D Plan A Plan B Plan C Plan D2004 40.8 40.8 40.8 40.8 40.8 0.0 0.0 0.0 0.02010 45.3 45.3 45.3 45.3 45.3 0.0 0.0 0.0 0.02020 50.8 50.8 50.8 52.5 54.1 0.0 0.0 1.7 3.32030 50.0 51.2 50.3 52.4 52.9 1.3 0.3 2.4 3.02040 51.3 55.7 52.3 54.9 53.9 4.4 1.0 3.6 2.62050 49.7 57.8 51.6 54.0 52.6 8.1 1.9 4.3 2.92060 46.1 57.3 48.7 50.2 48.9 11.2 2.6 4.2 2.82070 41.2 54.1 44.2 44.9 43.6 13.0 3.1 3.7 2.42080 35.9 48.8 38.9 39.1 38.0 12.9 3.0 3.2 2.12090 30.6 42.1 33.3 33.3 32.4 11.6 2.7 2.7 1.92100 26.2 36.2 28.5 28.5 27.7 10.0 2.3 2.3 1.6The additional costs are also shown in Table 5. If the additional cost is large, it is presumablyunsustainable. Plan D requires a minimum additional cost, at most 3.3 trillion yen or about 6% of thetotal expenditure. On the other hand, plan A requires a huge additional cost that exceeds 10 trillionyen or about 38% of the total expenditure in 2100. The additional cost of plan B or C is larger thanthat of plan D, but it is not huge like plan A. Therefore, plan A seems to be financially unrealistic,but other reform plans seem to be possible.5. ConclusionJapanese society cannot avoid rapid changes such as aging and a shift toward a depopulating society.With the increase in the number of elderly people, the need for social security is also increasing.How to efficiently distribute the revenue pie, feared to be shrinking, as social security benefits is animportant policy issue.We illustrated the prospects of the elderly in the future under the current pension scheme byusing the Japanese microsimulation model INAHSIM. It shows that the percentage of the elderlywith very low pension amount in the population will not increase. However, the percentage of thepoor elderly will increase by a large margin because of the changes in their families, such as theincrease in the number of elderly people living alone. The increasing numbers of the poor elderly inthe near future will be a serious problem for the Japanese society.Then, simulations were performed for four proposed pension reform plans. Plan A is a uniformearnings-related pension with a minimum guaranteed pension; plan B is a fully tax-funded basicpension system; plan C is a fully tax-funded basic pension system with accelerated transition23

measure; and plan D is a partially tax-funded basic pension system. The results show that all reformplans will reduce the number of poor elderly people significantly in the future. However, plans A andB are shown to have very little effect on reducing the number of poor elderly people by 2030. Othermeasures, probably difficult to introduce, would be necessary if we opt for plan A or B.Regarding additional costs, plan A requires huge costs, whereas the costs of Plan B, C, and Dare moderate. Plan A is unrealistic. Since plan B has the problem of the increasing the number ofpoor elderly people during the transition period, plans C and D are compelling reform measures.The author thinks that plan D is the best since it requires minimum additional costs and reducesthe number of poor elderly people immediately. In addition, plan D does not change the share of theburden between tax and insurance premium. Under the current scheme, the expenditure on basicpension is financed equally by taxes and insurance premium. Since about half of the elderly are intheir early stage and the other half, in their late stage, the share will remain unchanged. Thisadvantage can be used to build a consensus among stakeholders. Plan D, of course, meets the sevenprinciples framed by the government panel.In any case, evidence-based policy making is really important. Macro future estimates such aspopulation projections or the actuarial review of pension schemes are prepared by the government,while micro future estimates such as income distribution are not prepared. Micro future estimates arereally important for policy making. Without such micro-based estimates, it is very difficult toevaluate the income security function of the public pension scheme for the elderly. The governmentshould develop and use a microsimulation model like INAHSIM for more enhanced evidence-basedpolicy making.24

ReferencesAoi K., Okazaki Y., Fukawa T., and Hanada K. et al. (1986), Household projection by INAHSIM: Acomprehensive approach, Life Span vol. 6 (in Japanese).Fukawa T. (1994), “Future trends of Japanese households through micro simulation model: Anapplication of INAHSIM,” The Journal of Population Studies 18:13-27.Fukawa T. (2007), Household projection 2006/07 in Japan using a micro-simulation model, IPSSDiscussion Paper Series No.2007-E02.Fukawa T. (2009), “Household projections and its application to health/long-term care expendituresin Japan using INAHSIM-II,” paper presented to the second general conference of theInternational Microsimulation Association, Ottawa, June 8 - 10.Government Panel on New Public Pension Scheme (2010), "Basic Principles for the Nation’s NewPublic Pension Scheme (Interim Report)," http://www.npu.go.jp/policy/policy02/pdf/20100629/20100629_shinnenkinseido_haihu_1.pdf, accessed 11 January 2011 (in Japanese).Inagaki S. (1986), “An Analytical Model on Household and Family via Micro Simulation(INAHSIM),” Bulletin of the Institute of Actuaries of Japan 39:89-188 (in Japanese).Inagaki S. (2005), Projections of the Japanese Socio-Economic Structure Using a MicrosimulationModel (INAHSIM), IPSS Discussion Paper Series No.2005-03.Inagaki S. (2007a), Future Socio-Demographic Population Structure of Japan: Projections by adynamic Microsimulation Model (INAHSIM), Tokyo: Japan Statistical Association (inJapanese).Inagaki S. (2007b), “The Impact of the Increase in Non-regular Employment on Income Disparities,”Journal of Income Distribution 16:71-87.Inagaki S. (2010a), "Overview of INAHSIM: A Microsimulation Model for Japan," PIE/CISDiscussion Paper No. 468, Institute of Economic Research, Hitotsubashi University.Inagaki S. (2010b), "The Effects of Proposals for Basic Pension Reform on the Income Distributionof the Elderly in Japan,” The Review of Socionetwork Strategies,Vol.4,Springer Japan, pp.1-16.Inagaki S. (2010c), "Income Disparities and Behavior of People Born in 1950s–Outline and Analysisof Internet Survey on the Individual Records of Regular Pension Coverage Notice," PIE/CISDiscussion Paper No. 495, Institute of Economic Research, Hitotsubashi University.Inagaki, S. (2010d), "The Effect of Proposals for Basic Pension Reform on the Income Distributionof the Elderly in Japan," Review of Socionetwork Strategies, 4, pp.1-16.25

Inagaki S. and Kaneko N. (2008), “Projections of Income Distribution using a MicrosimulationModel (INAHSIM),” Fiscal 2007 Report for Research on Social Security that Pays Attention tothe Relationship between Income/Property/Consumption and Contribution/Taxes, 383-410 (inJapanese).Kaneko, R., Ishikawa, A., Ishii, F., Sakai, S., Iwasawa, M., Mita, F., Moriizumi, R. (2008),"Population Projections for Japan: 2006–2055 Outline of Results, Methods, andAssumptions," The Japanese Journal of Population 6(1), pp.76-114.Ministry of Health, Labor and Welfare, Pension Bureau, Actuarial Division (2010), "The 2009Actuarial Valuation of the Employees' Pension Insurance and the National Pension," Report ofthe 2009 Actuarial Valuation, pp.33-48.National Council for Social Security (2008), "National Council for Social Security Interim Report,"http://www.kantei.go.jp/jp/singi/syakaihosyoukokuminkaigi/chukan/siryou1.pdf, accessed 20April 2010 (in Japanese).National Institute of Population and Social Security Research (2008), Household Projections forJapan: 2005–2030, Health and Welfare Statistics Association (in Japanese).Takayama, N. (2010), Public Pension and Child allowance, Iwanami Press (in Japanese).26

Appendix A: Model population1. Database structureThe model population comprises three tables of data that are referred to as the “individual segment,”the “couple segment,” and the “household segment.” There are linkages between the individualsegment and the couple segment, to represent a family; there are also linkages between the individualsegment and the household segment, to represent a household.(1) Person ID (PID) is assigned to each person in the individual segment.(2) Couple ID (CID) is assigned to each couple in the couple segment.(3) Household ID (HID) is assigned to each household in the household segment.Figure 12: Database structureIndividual segmentParent–child relationshipHusband and wifeCo-resident relationshipCouple segmentHousehold segment27

2. Contents of each segmentTable 6: Individual segmentNo. Item Description1 Couple IDCID if he/she is marriedZero if he/she is not married2 Household IDHID to which he/she belongsZero if he/she is deceased3List structure of a PID of his/her next elderly sibling, if he/she is not an eldest childfamilyZero if he/she is the eldest4List structure of a PID of his/her household’s memberhouseholdZero if he/she is the last member of the household5 ParentCouple ID number of his/her parentZero if his/her parent is unknown (not alive at the initialpopulation)6 Ex-couple IDEx-couple ID number if he/she experienced divorce or death ofspouseZero if he/she experienced neither7 Dummy8 Year of birth Year of birth9 Year of death Year of death10 Sex 1: male; 2: female11 Health status 1: good; 2: poor12 Employment status1: regular employed; 2: non-regular employed;3: self-employed; 4: non-employed13Subscription category 1: category_1; 2: category_2; 3: category_3;of public pension 4: non-subscriber14 Marital status 1: married; 2: never married; 3: divorced; 4: widow/widower15 Nationality 1: Japanese; 2: non-Japanese161: paid; 2: partially exemption (1/4);Payment of pension3: partially exemption (1/2); 4: partially exemption (3/4);premium5: full exemption; 6: moratorium; 7: not paid17–19 Dummy20 z-score z-score of earnings21 Pension benefit1: basic pension only; 2: earnings-related pension only;3: both; 4: none28

Table 7: Individual segment (cont.)No. Item Description22 Lifetime income Lifetime income after year 200423Employment status atage 35Employment status at age 3524Subscription categoryat age 35Subscription category of public pension at age 3525 z-score at age 35 z-score at age 351: parasite single at age 35 (non-regular employed or nonemployed);26Parasite status at age352: parasite single at age 35 (regular employed or self-employed);3: non-parasite single at age 3527Employment status inthe previous yearEmployment status in the previous year28 Equivalent income Equivalent income29Marital status in theprevious yearMarital status in the previous year30 Dummy31 Total income Total income32 Earnings Earnings33 Basic pension Basic pension34Earnings-relatedpensionEarnings-related pension35 Other income Other income36–40 Dummy41 Period of category 1 Insured period of category 1 (excluding non-payment period)42 Period of category 2 Insured period of category 243 Period of category 3 Insured period of category 344Insured period for Premium-paid period of category 1, plus insured period ofbasic pension category 2 or 345–100 Dummy29

Table 8: Couple segmentNo. Item Description1Person ID of the PID of the youngest childyoungest child Zero if the couple has no child2 Person ID of husbandPID of husbandZero if husband is unknown3 Person ID of wifePID of wifeZero if wife is unknown4 Year of marriage Year of marriage5 Year of dissolutionYear of dissolutionZero if the couple is active6Number of ever-bornchildrenNumber of ever-born children7Number of survivingchildrenNumber of surviving children8Reason for3: divorced; 4: widowed/widowereddissolutionZero if the couple is active9Number of noncoresidentchildrenNumber of non-coresident children10 Dummy11 Earnings of couple Total earnings of the couple at wife's age 5012 Pension of couple Total pension benefit of the couple at wife's age 7013 Replacement rateRatio of pension benefit (item No. 12) to earnings (item No. 11),–1 if age of wife is

Table 9: Household segmentNo. Item Description1Person ID of PID of one of the household membershousehold member Zero if the household is dissolved2 Dummy3 Year formed Year the household was formed4 Year of dissolutionYear of dissolutionZero if the household is active5 Household size Number of household members6 Type of household 1: private household; 2: institution7 Household structure1: single household; 2:couple-only household; 3: couple andunmarried children; 4: single parent and children; 5: threegeneration household; 6: other private household; 7: institutionZero if the household is dissolved8 Total earnings Total earnings of household9 Total pension benefit Total pension benefit of household10 Total other incomes Total other incomes of household11 Total incomes Total incomes of household12–20 Dummy31

3. Linkages among the segments3.1. Family: parent–child relationshipsFigure 13: Parents (couple segment) to children (individual segment)P1 3Couple segment: CID=C1Number of ever-born childrenP2C1Individual segment: PID=P1P3C1Individual segment: PID=P20C1Individual segment: PID=P3Figure 14: Children (individual segment) to parents (couple segment)P1 3Couple segment: CID=C1Number of ever-born childrenP2C1Individual segment: PID=P1P3C1Individual segment: PID=P20C1Individual segment: PID=P332

3.2. Couple: husband and wifeFigure 15: Couple (couple segment) to husband and wife (individual segment)P4P5Couple segment: CID=C1HusbandWifeC1Individual segment: PID=P4C1Individual segment: PID=P5Figure 16: Husband and wife (Individual segment) to couple (couple segment)P4P5Couple segment: CID=C1C1Individual segment: PID=P4C1Individual segment: PID=P5Figure 17: Widow (Individual segment) to ex-couple (couple segment)P6 4Couple segment: CID=C2Reason for dissolutionC2Individual segment: PID=P633

3.3. Household: household and household membersFigure 18: Household (household segment) to household members (Individual segment)P7 3Household segment: HID=H1Number of household membersH1P8Individual segment: PID=P7H1P9Individual segment: PID=P8H10Individual segment: PID=P9Figure 19: Household members (individual segment) to household (household segment)P7 3Household segment: HID=H1Number of household membersH1P8Individual segment: PID=P7H1P9Individual segment: PID=P8H10Individual segment: PID=P934

Appendix B: Life events and transition probabilitiesLife event Transition probabilities Source and notesMarriageBirthDeathDivorceInternationalmigrationChange inhealth statusProbability of first marriage(remarriages), by age, sex andemployment statusProbability of co-residencywith parents at marriageProbability of maritalfertility, by mother’s age andnumber of ever-born childrenProbability of death by age,sex, and health statusProbability of divorce, bywife’s age, with or withoutchildrenProbability of divorcedindividuals returning to theirparents’ home after divorce,by sexProbability of custody by sexNumber of immigrants by sexand ageThe z-score is assignedrandomly.Transition probability ofhealth status (good or poor)by age and sexSource: Vital statistics 2005Relative risk of first marriage for non-regularemployed men: 0.558Decreasing trends by 2010Source: CSLC 2001Groom’s parents: 0.2Bride’s parents: 0.05Source: Vital statistics 2005His/her z-score assigned, based on parents’ z-scoresSame assumptions as the official populationprojectionsRelative risk of death for health status notconsidered for this simulationDecreasing trends by 2055Source: Vital statistics 2005Relative risk of divorce for those withoutchildren: 1.488Source: CSLC 2001Male: 0.43Female: 0.35Source: Vital statistics 2005Male:0.2Female:0.8The same assumptions as the official populationprojectionsNet number of migrants taken into accountAll immigrants assumed to be singleSource: CSLC 2001Estimate the probabilities of keeping thepercentages of health status, by sex and age35

Change inemploymentstatusEstimatingearningsDeterminingpensionsTransition probability ofemployment status, by sex,age, and marital statusDistribution of earnings byage, sex and marital statusDistribution of newlyawarded pension amounts byage and sex of subscriptioncategory at age 35Transition probabilities between regularemployedand other statuses: The sameassumptions of the official actuarial revaluationof the public pension schemeOther transition probabilities: Estimate ofprobabilities that percentages of employmentstatus, other than regular employed, will remainconstant. (Source: CSLC 2004)Relative risks are considered for women whohave a first baby, who are living with theirparents, or who just got married. (Source:Inagaki, 2007)Source: CSLC 2004Estimate of the parameters of log-normaldistributionSource: Inagaki (2010)Estimate based on individual records from theregular pension coverage report.Source: CSLC 2001Young Probabilities of leaving/Estimate of probabilities that percentages ofpeople returning home, by age, sex,young, never-married children co-resident withleaving home and employment statustheir parents will remain constant.Source: CSLC 2001Living withProbability of living with Estimate of probabilities that percentages ofelderlychildren, by age and sex children co-resident with elderly parents willparentsremain constant.Probability of entering an Source: Population Census 2005Entering aninstitution, by sex, age, and Estimate of probabilities that percentages of theinstitutionmarital statuselderly in institutions will remain constant.Payment of Probability of paying theSource: the annual report on the pension planpension premium of the Nationalprepared by the Social Security Agencypremium Pension.Note: Comprehensive Survey of Living Conditions, conducted by Ministry of Health, Labor, andWelfare.36

Appendix C: Modules and CPU times1. Brief overview of flow chartStartInput parametersParametersRead transition probabilitiesLoop for simulation repeatingTransitionprobabilitiesRead initial populationLoop for simulationInitialpopulationSimulate life eventsStatisticsEnd of loopLongitudinalmicro-leveldataStatisticsEnd of loopStatisticsWrite StatisticsEnd37

2. Subroutines2.1. Definition of variablesNo. Module File name Description LinesC01 com_var C01_var.f90 Key parameters of database 34C02 com_prob C02_prob.f90 Transition probabilities 56C03 com_table C03_table.f90 Tables of statistics 113C04 com_title C04_title.f90 Titles of statistics 155C05 com_item C05_item.f90 Items of statistics 2242.2. Main programNo. Module File name Description Lines- Main Main.f90 Main program 3552.3. Subsystems for life eventsNo. Module File name Description LinesL11 kekkon L11_kekkon.f90 Marriage 199L12 shussho L12_shussho.f90 Birth 117L13 shibo L13_shibo.f90 Death 174L14 rikon L14_rikon.f90 Divorce 180L15 imin L15_imin.f90 Immigration 77L21 kaigo L21_kaigo.f90 Change in health status 60L31 shugyo L31_shugyo.f90 Change in employment status 218L32 unemp L32_Unemp.f90 Non-regular job losses in 2009 42L41 kado L41_kado.f90 Estimating earnings 102L51 nenkin L51_nenkin.for Determining pensions 241L61 rika L61_rika.f90 Young people leaving home 67L62 gappei L62_gappei.f90 Living with elderly parents 76L63 shisestu L63_shisestu.for Entering an institution 135L71 zeikin L71_zeikin.f90 Payment of pension premium 9038

2.4. StatisticsNo. Module File name Description LinesS01 stat01 S01_person.f90 Statistics for individual segments 345S02 stat02 S02_couple.f90 Statistics for couple segments 91S03 stat03 S03_house.f90 Statistics for household segments 130S04 stat04 S04_statictic.f90 Secondary statistics 100S05 stat05 S05_cohort.f90 Cohort statistics 109S07 stat_chk S07_chk.f90 Statistics for debug 39S11 ginicov S11_ginicov.f90 Gini coefficient, quartile 62S21a tashikomi S21_tab.f90 Summation of each round 129S21b kakidashi S21_tab.f90 Write tables 129S21c goukei S21_tab.f90 Summation of tables 104S22a sumup1 S22_sum.f90 Summation of a one-dimensional table 23S22b sumup2 S22_sum.f90 Summation of a two-dimensional table 23S22c sumup3 S22_sum.f90 Summation of a three-dimensional table 34S22d segout S22_sum.f90 Output micro data 26S22e kaku S22_sum.f90 Calculation of average, output tables 38S23 clear01 S23_clear01.f90 Zero clear of tables 130S24 clear02 S24_clear02.f90 Zero clear of tables for inclusive sum 1302.5. Update of characteristicsNo. Module File name Description LinesU01a up_zennen U01_update.f90Update the characteristics of the previousyear27U01b up_income U01_update.f90 Recalculation of total incomes 108U01c up_house U01_update.f90 Determination of household structure 23U02 history U02_history.f90 Personal history 2739

2.6. Auxiliary routinesNo. Module File name Description LinesA01 dokyo1 Sub_lib.f90Identify whether a person lives with his orher parents50A02 dokyo2 Sub_lib.f90Identify whether a person's parents belongto a specified household33A03 dokyo3 Sub_lib.f90Identify a pattern of co-residency withparents55A04 haigu Sub_lib.f90 Find PID of current spouse 27A05 haiguk Sub_h.for Find marital status 34A06 jikka Sub_h.for Find HID of parental house 40A07 joshi Sub_lib.f90Classify a female by co-residency with herparents, experience of birth, and experience 53of marriageA08 keitai Sub_lib.f90 Identify a family type of the elderly 63A09 kozo Sub_h.for Identify household structure 116A10 ksetai Sub_h.forIdentify whether a household contains aelderly person26A11 oya Sub_lib.f90 Find CID of parents 24A12 oyako Sub_lib.f90 Identify parent–child relationship 28A13 sagasu Sub_lib.f90 Find PID of a surviving children 42A14 parasite Sub_lib.f90Identify whether a person is a parasitesingle42A15 psetai Sub_h.forIdentify whether a household contains aparasite-single aged over 2527A16 ruikei Sub_h.for Identify household type 92A17 sampling L11_kekkon.f90Systematic sampling of marriagecandidates32A18 seikubun Sub_lib.f90 Classify a person by sex and marital status 27A19 sub_seed Sub_seed.f90 Set random seeds 17A20 under20 Sub_lib.f90 Count number of children with custody 2540

2.7. Manipulation of databaseNo. Module File name Description LinesB01 birth Sub_b.f90Add a newborn baby to a list structure of afamily31B02 death Sub_b.f90Add year of death (death flag) to individualsegment17B03 increase Sub_b.f90Add a household member to a list structureof a household17B04 produce Sub_b.f90 Form a new household 18B05 decrease Sub_b.f90Delete a household member from a liststructure of a householdAdd year of extinction (extinction flag) to a41householdB06 extinct Sub_b.f90 Dissolution of a couple 28B07 marriage Sub_b.f90 Create a new couple 24B08 immigrate Sub_b.f90 Add an immigrant to individual segment 15B09 move20 Sub_b.f90Move children under 20 to theirmother/father's household3641

2.8. Read transition probabilitiesNo. Module File name Description LinesT01a kiso_bir T01_demo.f90 Read transition probabilities for “Birth” 67T01b kiso_dea T01_demo.f90 Read transition probabilities for “Death” 50T01c kiso_mar T01_demo.f90 Read transition probabilities for “Marriage” 71T01d kiso_div T01_demo.f90 Read transition probabilities for “Divorce” 71T01e kiso_mig T01_demo.f90Read transition probabilities for“Immigration”41T02 kiso_ken T02_ken.f90Read transition probabilities for “Change inhealth status”32T03 kiso_emp T03_emp.f90Read transition probabilities for “Change inemployment status”87T04 kiso_ear T04_ear.f90Read transition probabilities for“Estimating earnings”44T05 kiso_pen T05_pen.f90Read transition probabilities for“Determining pensions”65T06a kiso_sin T06_house.f90Read transition probabilities for “Youngpeople leaving home”55T06b kiso_old T06_house.f90Read transition probabilities for “Livingwith elderly parents”42T11 kiso_eco T11_eco.f90 Read macro-economic indicators 482.9. Data checkNo. Module File name Description LinesD01 p_check D01_person.f90 Logical check of individual segments 65D04 check D04_database.for Logical check of database 310D05 p_dump D05_dump.f90 Output dump list of individual segments 2342

3. CPU timeCall Stack CPU Time Call Stack CPU TimeMAIN__ 52.869 s STAT05 0.990 sSTAT01 740.698 s CLEAR02 0.660 sCHECK 275.115 s for_read_seq_lis 0.250 sSHUGYO 250.716 s IMIN 0.060 sUP_INCOME 240.387 s for_write_seq_lis_xmit 0.020 sKADO 142.337 s CLEAR01 0.020 sSHISETSU 79.139 s cvt_ieee_t_to_text_ex 0.010 sZEIKIN 69.208 s KISO_PEN 0.010 sHISTORY 57.691 s KISO_MAR 0.010 sSHIBO 54.952 s KISO_KEN 0 sSTAT03 47.186 s KISO_SIN 0 sNENKIN 46.242 s KISO_OLD 0 sSTAT_CHK 44.996 s KISO_EAR 0 sGAPPEI 44.412 s KISO_EMP 0 sP_CHECK 43.476 s KISO_MIG 0 sUP_ZENNEN 42.590 s KISO_DIV 0 sRIKA 42.460 s KISO_DEA 0 sKEKKON 40.393 s KISO_BIR 0 sKAIGO 38.414 s for_open 0 sSTAT02 26.518 s GOUKEI 0 sRIKON 24.780 s for_read_seq_fmt 0 sfor_read_seq_lis_xmit 23.829 s for_write_seq_fmt_xmit 0 sSHUSSHO 19.330 s for_write_seq_lis 0 sUP_HOUSE 9.346 s STAT04 0 sTASHIKOMI 1.198 s KAKIDASHI 0 sNote: Analyzed by Intel Vtune Amplifier XE(1) Simulation period: 2004–2100 (95years)(2) Repeated simulations: 100 iterations(3) CPU: Intel Core i7 975 Extreme edition 3.33GHz(4) Memory: 12GB(5) The above CPU times were measured in debug mode. The CPU times in release mode willbe much shorter.43

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